A little about me: I am an associate professor of Statistics and Electrical Engineering at Stanford University. I completed my PhD in computer science at Berkeley in 2014. My research interests are a bit eclectic, and they span computation, statistics, optimization, and machine learning; if you like any of these, we can probably find something interesting to chat about. (Here is a slightly more formal bio, with higher resolution photos, in the third-person.)
Publications: [a list of my publications] in mostly chronological order.
Curriculum Vitae: [cv.pdf]
Contact info: [Visit]
Please note: unfortunately, I am unable to respond to most inquiries regarding openings for graduate and postdoctoral positions in my group. Admissions to Stanford are handled at a department-wide level, not by me individually, so I am unable to comment on your suitability for graduate school or work with me. If you are already a Stanford student or have been admitted to Stanford, feel free to contact me about interests we may share.
EE 364m: Mathematics of Convexity (Winter 2024)
Engineering 108: Introduction to Matrix Methods (Spring 2023, Fall 2023)
Statistics 305a: Applied Statistics I (Fall 2022, Fall 2021)
Statistics 116: Introduction to Probability (Spring 2021)
Statistics 311/Electrical Engineering 377: Information Theory and Statistics (Fall 2014, Winter 2016, Winter 2019, Fall 2021, Fall 2023)
Electrical Engineering 364a: Convex Optimization (Winter 2020, 2021)
Statistics 300b: Theory of Statistics (Winter 2017, 2018, 2019, 2021)
Statistics 101: Data Science (Autumn 2018)
CS 229T/Statistics 229T: Machine Learning Theory (Autumn 2017)
Electrical Engineering 364b: Convex Optimization II (Spring 2015, Spring 2018)
CS/Stats 229: Machine learning (Spring 2016, Autumn 2016)